support roi_align & affine_channel for kunlun (#29561)
* support roi_align & affine_channel for kunlun * minorrevert-31562-mean
QingshuChen
4 years ago
committed by
GitHub
parent
0cad1152f4
commit
79a41a9ed6
@ -0,0 +1,186 @@
|
|||||||
|
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
|
||||||
|
Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
you may not use this file except in compliance with the License.
|
||||||
|
Indicesou may obtain a copy of the License at
|
||||||
|
|
||||||
|
http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
|
||||||
|
Unless required by applicable law or agreed to in writing, software
|
||||||
|
distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
See the License for the specific language governing permissions and
|
||||||
|
limitations under the License. */
|
||||||
|
|
||||||
|
#ifdef PADDLE_WITH_XPU
|
||||||
|
|
||||||
|
#include <string>
|
||||||
|
#include <unordered_map>
|
||||||
|
#include <vector>
|
||||||
|
#include "paddle/fluid/framework/data_layout.h"
|
||||||
|
#include "paddle/fluid/framework/eigen.h"
|
||||||
|
#include "paddle/fluid/framework/op_registry.h"
|
||||||
|
|
||||||
|
namespace paddle {
|
||||||
|
namespace operators {
|
||||||
|
|
||||||
|
template <typename DeviceContext, typename T>
|
||||||
|
class AffineChannelXPUKernel : public framework::OpKernel<T> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto* x = ctx.Input<framework::Tensor>("X");
|
||||||
|
auto* scale = ctx.Input<framework::Tensor>("Scale");
|
||||||
|
auto* bias = ctx.Input<framework::Tensor>("Bias");
|
||||||
|
|
||||||
|
auto* y = ctx.Output<framework::Tensor>("Out");
|
||||||
|
y->mutable_data<T>(ctx.GetPlace());
|
||||||
|
|
||||||
|
const framework::DataLayout layout =
|
||||||
|
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
|
||||||
|
|
||||||
|
auto dims = x->dims();
|
||||||
|
int N = dims[0];
|
||||||
|
int C = layout == framework::DataLayout::kNCHW ? dims[1]
|
||||||
|
: dims[dims.size() - 1];
|
||||||
|
int HxW = x->numel() / N / C;
|
||||||
|
|
||||||
|
auto* scale_d = scale->data<T>();
|
||||||
|
auto* bias_d = bias->data<T>();
|
||||||
|
|
||||||
|
auto* x_d = x->data<T>();
|
||||||
|
auto* y_d = y->data<T>();
|
||||||
|
auto& dev_ctx = ctx.template device_context<DeviceContext>();
|
||||||
|
std::vector<int> x_shape;
|
||||||
|
std::vector<int> b_shape;
|
||||||
|
if (layout == framework::DataLayout::kNCHW) {
|
||||||
|
x_shape.push_back(N);
|
||||||
|
x_shape.push_back(C);
|
||||||
|
x_shape.push_back(HxW);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
b_shape.push_back(C);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
} else {
|
||||||
|
x_shape.push_back(N * HxW);
|
||||||
|
x_shape.push_back(C);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
b_shape.push_back(C);
|
||||||
|
}
|
||||||
|
int r = 0;
|
||||||
|
r = xpu::broadcast_mul(dev_ctx.x_context(), x_d, scale_d, y_d, x_shape,
|
||||||
|
b_shape);
|
||||||
|
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External(
|
||||||
|
"The broadcast_mul XPU OP return wrong value[%d %s]",
|
||||||
|
r, XPUAPIErrorMsg[r]));
|
||||||
|
r = xpu::broadcast_add(dev_ctx.x_context(), y_d, bias_d, y_d, x_shape,
|
||||||
|
b_shape);
|
||||||
|
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External(
|
||||||
|
"The broadcast_add XPU OP return wrong value[%d %s]",
|
||||||
|
r, XPUAPIErrorMsg[r]));
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
template <typename DeviceContext, typename T>
|
||||||
|
class AffineChannelGradXPUKernel : public framework::OpKernel<T> {
|
||||||
|
public:
|
||||||
|
void Compute(const framework::ExecutionContext& ctx) const override {
|
||||||
|
auto* x = ctx.Input<framework::Tensor>("X");
|
||||||
|
auto* scale = ctx.Input<framework::Tensor>("Scale");
|
||||||
|
auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
|
||||||
|
|
||||||
|
auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
|
||||||
|
auto* dscale =
|
||||||
|
ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
|
||||||
|
auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
|
||||||
|
|
||||||
|
const framework::DataLayout layout =
|
||||||
|
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
|
||||||
|
|
||||||
|
auto dims = x->dims();
|
||||||
|
int N = dims[0];
|
||||||
|
int C = layout == framework::DataLayout::kNCHW ? dims[1]
|
||||||
|
: dims[dims.size() - 1];
|
||||||
|
int HxW = x->numel() / N / C;
|
||||||
|
|
||||||
|
auto* dy_d = dy->data<T>();
|
||||||
|
auto* scale_d = scale->data<T>();
|
||||||
|
|
||||||
|
T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
||||||
|
T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
||||||
|
T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
|
||||||
|
|
||||||
|
auto& dev_ctx = ctx.template device_context<DeviceContext>();
|
||||||
|
std::vector<int> x_shape;
|
||||||
|
std::vector<int> b_shape;
|
||||||
|
std::vector<int> rdims;
|
||||||
|
if (layout == framework::DataLayout::kNCHW) {
|
||||||
|
x_shape.push_back(N);
|
||||||
|
x_shape.push_back(C);
|
||||||
|
x_shape.push_back(HxW);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
b_shape.push_back(C);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
rdims.push_back(0);
|
||||||
|
rdims.push_back(2);
|
||||||
|
} else {
|
||||||
|
x_shape.push_back(N * HxW);
|
||||||
|
x_shape.push_back(C);
|
||||||
|
b_shape.push_back(1);
|
||||||
|
b_shape.push_back(C);
|
||||||
|
rdims.push_back(0);
|
||||||
|
}
|
||||||
|
|
||||||
|
int r = 0;
|
||||||
|
if (dscale_d && dbias_d) {
|
||||||
|
r = xpu::reduce_sum<T>(dev_ctx.x_context(), dy_d, dbias_d, x_shape,
|
||||||
|
rdims);
|
||||||
|
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External(
|
||||||
|
"The reduce_sum XPU OP return wrong value[%d %s]",
|
||||||
|
r, XPUAPIErrorMsg[r]));
|
||||||
|
T* tmp = nullptr;
|
||||||
|
r = xpu_malloc(reinterpret_cast<void**>(&tmp), dy->numel() * sizeof(T));
|
||||||
|
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External("no enough memory in xpu"));
|
||||||
|
|
||||||
|
r = xpu::mul<T>(dev_ctx.x_context(), dy_d, x->data<T>(), tmp,
|
||||||
|
dy->numel());
|
||||||
|
PADDLE_ENFORCE_EQ(
|
||||||
|
r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External("The mul XPU OP return wrong value[%d %s]",
|
||||||
|
r, XPUAPIErrorMsg[r]));
|
||||||
|
r = xpu::reduce_sum<T>(dev_ctx.x_context(), tmp, dscale_d, x_shape,
|
||||||
|
rdims);
|
||||||
|
PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External(
|
||||||
|
"The reduce_sum XPU OP return wrong value[%d %s]",
|
||||||
|
r, XPUAPIErrorMsg[r]));
|
||||||
|
if (dev_ctx.x_context()->xpu_stream) {
|
||||||
|
dev_ctx.Wait();
|
||||||
|
}
|
||||||
|
xpu_free(tmp);
|
||||||
|
}
|
||||||
|
if (dx_d) {
|
||||||
|
r = xpu::broadcast_mul(dev_ctx.x_context(), dy_d, scale_d, dx_d, x_shape,
|
||||||
|
b_shape);
|
||||||
|
PADDLE_ENFORCE_EQ(
|
||||||
|
r, xpu::Error_t::SUCCESS,
|
||||||
|
platform::errors::External(
|
||||||
|
"The broadcast_mul XPU OP return wrong value[%d %s]", r,
|
||||||
|
XPUAPIErrorMsg[r]));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
} // namespace operators
|
||||||
|
} // namespace paddle
|
||||||
|
|
||||||
|
namespace ops = paddle::operators;
|
||||||
|
using XPU = paddle::platform::XPUDeviceContext;
|
||||||
|
|
||||||
|
REGISTER_OP_XPU_KERNEL(affine_channel, ops::AffineChannelXPUKernel<XPU, float>);
|
||||||
|
REGISTER_OP_XPU_KERNEL(affine_channel_grad,
|
||||||
|
ops::AffineChannelGradXPUKernel<XPU, float>);
|
||||||
|
|
||||||
|
#endif
|
||||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,148 @@
|
|||||||
|
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
"""
|
||||||
|
Unit testing for affine_channel_op
|
||||||
|
"""
|
||||||
|
|
||||||
|
from __future__ import print_function
|
||||||
|
|
||||||
|
import sys
|
||||||
|
sys.path.append("..")
|
||||||
|
|
||||||
|
import unittest
|
||||||
|
import numpy as np
|
||||||
|
from op_test_xpu import XPUOpTest
|
||||||
|
import paddle
|
||||||
|
import paddle.fluid.core as core
|
||||||
|
import paddle.fluid as fluid
|
||||||
|
|
||||||
|
|
||||||
|
def affine_channel(x, scale, bias, layout):
|
||||||
|
C = x.shape[1] if layout == 'NCHW' else x.shape[-1]
|
||||||
|
if len(x.shape) == 4:
|
||||||
|
new_shape = (1, C, 1, 1) if layout == 'NCHW' else (1, 1, 1, C)
|
||||||
|
else:
|
||||||
|
new_shape = (1, C)
|
||||||
|
scale = scale.reshape(new_shape)
|
||||||
|
bias = bias.reshape(new_shape)
|
||||||
|
return x * scale + bias
|
||||||
|
|
||||||
|
|
||||||
|
class TestAffineChannelOp(XPUOpTest):
|
||||||
|
def setUp(self):
|
||||||
|
self.op_type = "affine_channel"
|
||||||
|
self.init_test_case()
|
||||||
|
|
||||||
|
x = np.random.random(self.shape).astype("float32")
|
||||||
|
scale = np.random.random(self.C).astype("float32")
|
||||||
|
bias = np.random.random(self.C).astype("float32")
|
||||||
|
|
||||||
|
y = affine_channel(x, scale, bias, self.layout)
|
||||||
|
|
||||||
|
self.inputs = {'X': x, 'Scale': scale, 'Bias': bias}
|
||||||
|
self.attrs = {'data_layout': self.layout}
|
||||||
|
self.outputs = {'Out': y}
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
if core.is_compiled_with_xpu():
|
||||||
|
paddle.enable_static()
|
||||||
|
place = paddle.XPUPlace(0)
|
||||||
|
self.check_output_with_place(place)
|
||||||
|
|
||||||
|
def test_check_grad(self):
|
||||||
|
if core.is_compiled_with_xpu():
|
||||||
|
paddle.enable_static()
|
||||||
|
place = paddle.XPUPlace(0)
|
||||||
|
self.check_grad_with_place(place, ['X', 'Scale', 'Bias'], 'Out')
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dx(self):
|
||||||
|
if core.is_compiled_with_xpu():
|
||||||
|
paddle.enable_static()
|
||||||
|
place = paddle.XPUPlace(0)
|
||||||
|
self.check_grad_with_place(
|
||||||
|
place, ['Scale', 'Bias'], 'Out', no_grad_set=set('X'))
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dscale_dbias(self):
|
||||||
|
if core.is_compiled_with_xpu():
|
||||||
|
paddle.enable_static()
|
||||||
|
place = paddle.XPUPlace(0)
|
||||||
|
self.check_grad_with_place(
|
||||||
|
place, ['X'], 'Out', no_grad_set=set(['Scale', 'Bias']))
|
||||||
|
|
||||||
|
def init_test_case(self):
|
||||||
|
self.shape = [2, 100, 3, 3]
|
||||||
|
self.C = 100
|
||||||
|
self.layout = 'NCHW'
|
||||||
|
|
||||||
|
|
||||||
|
class TestAffineChannelOpError(unittest.TestCase):
|
||||||
|
def test_errors(self):
|
||||||
|
with fluid.program_guard(fluid.Program()):
|
||||||
|
|
||||||
|
def test_x_type():
|
||||||
|
input_data = np.random.random(2, 1, 2, 2).astype("float32")
|
||||||
|
fluid.layers.affine_channel(input_data)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_x_type)
|
||||||
|
|
||||||
|
def test_x_dtype():
|
||||||
|
x2 = fluid.layers.data(
|
||||||
|
name='x2', shape=[None, 1, 2, 2], dtype='int32')
|
||||||
|
fluid.layers.affine_channel(x2)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_x_dtype)
|
||||||
|
|
||||||
|
def test_scale_type():
|
||||||
|
x3 = fluid.layers.data(
|
||||||
|
name='x3', shape=[None, 1, 2, 2], dtype='float32')
|
||||||
|
fluid.layers.affine_channel(x3, scale=1)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_scale_type)
|
||||||
|
|
||||||
|
def test_bias_type():
|
||||||
|
x4 = fluid.layers.data(
|
||||||
|
name='x4', shape=[None, 1, 2, 2], dtype='float32')
|
||||||
|
fluid.layers.affine_channel(x4, bias=1)
|
||||||
|
|
||||||
|
self.assertRaises(TypeError, test_bias_type)
|
||||||
|
|
||||||
|
|
||||||
|
class TestAffineChannelNHWC(TestAffineChannelOp):
|
||||||
|
def init_test_case(self):
|
||||||
|
self.shape = [2, 3, 3, 100]
|
||||||
|
self.C = 100
|
||||||
|
self.layout = 'NHWC'
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dx(self):
|
||||||
|
return
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dscale_dbias(self):
|
||||||
|
return
|
||||||
|
|
||||||
|
|
||||||
|
class TestAffineChannel2D(TestAffineChannelOp):
|
||||||
|
def init_test_case(self):
|
||||||
|
self.shape = [2, 100]
|
||||||
|
self.C = 100
|
||||||
|
self.layout = 'NCHW'
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dx(self):
|
||||||
|
return
|
||||||
|
|
||||||
|
def test_check_grad_stopgrad_dscale_dbias(self):
|
||||||
|
return
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue